Object attribution analyzing method and related object attribution analyzing device

ABSTRACT

An object attribution analyzing method applied to an object attribution analyzing device and includes dividing a plurality of continuous frames into a current frame and several previous frames, utilizing face detection to track and compute a first attribution predicted value of an object within the current frame, utilizing the face detection to acquire a feature parameter of the object within the current frame for setting a first weighting, acquiring a second attribution predicted value of the object within the several previous frames, setting a second weighting in accordance with the first weighting, and generating a first induction attribution predicted value of the object within the plurality of continuous frames via the first attribution predicted value weighted by the first weighting and the second attribution predicted value weighted by the second weighting.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to an object attribution analyzing methodand a related object attribution analyzing device, and moreparticularly, to an object attribution analyzing method and a relatedobject attribution analyzing device capable of increasing its accuracyreferring to previous analyzing information.

2. Description of the Prior Art

Please refer to FIG. 1. FIG. 1 is a diagram of variation of anattribution predicted value of an object within continuous frames outputby the conventional camera in the prior art. The conventional camera hasan image analyzing function of determining a specific attribution of theobject from one frame. The object can be a human or a vehicle inside amonitoring image, and the specific attribution can be an invariableproperty of the object. For example, the specific attribution can begender, ages, the color of skin or hair, or a height of the human, or abrand or color of the vehicle. As an example of the human, when thehuman walks through a monitoring area of the camera, the camera cangenerate a plurality of continuous frames Is, which contain a pattern ofthe human. An angle or a distance of the human relative to the cameramay be changed or the human may be sheltered by building when the humanwalks, which results in violent change of the attribution predictedvalue of the same object within the several frames; in the meantime, anattribution predicted value output by the conventional camera isirregularly varied. A curve C1 can represent variation of theattribution predicted value within the continuous frames Is. Thepredicted value is irregularly increased and decreased within differentframes. The attribution predicted result lower than the attributionpredicted value P may define the gender of male, and the attributionpredicted result higher than the attribution predicted value P maydefine the gender of female. Although the object traced within theplurality of continuous frames Is should be the same person, thepredicting function of the conventional camera cannot confirm itscorrect attribution among different frames. For example, theconventional camera may output an identifying result of the human beinga male due to some frames, and then output another identifying result ofthe human being a female due to other frames; the conventional cameracannot provide correct identifying solution. Therefore, design of anidentifying technology capable of effectively increasing objectattribution predicted accuracy is an important issue in the monitoringindustry.

SUMMARY OF THE INVENTION

The present invention provides an object attribution analyzing methodand a related object attribution analyzing device capable of increasingits accuracy referring to previous analyzing information for solvingabove drawbacks.

According to the claimed invention, an object attribution analyzingmethod includes dividing a plurality of continuous frames into a currentframe and several previous frames, utilizing face detection to track andcompute a first attribution predicted value of an object within thecurrent frame, utilizing the face detection to acquire a featureparameter of the object within the current frame for setting a firstweighting, acquiring a second attribution predicted value of the objectwithin the several previous frames, setting a second weighting inaccordance with the first weighting, and generating a first inductedattribution predicted value of the object within the plurality ofcontinuous frames via the first attribution predicted value weighted bythe first weighting and the second attribution predicted value weightedby the second weighting.

According to the claimed invention, an object attribution analyzingdevice includes a receiver and a processor. The receiver is adapted toreceive a plurality of continuous frames. The processor is electricallyconnected to the receiver. The processor is adapted to divide theplurality of continuous frames into a current frame and several previousframes, utilize face detection to track and compute a first attributionpredicted value of an object within the current frame, utilize the facedetection to acquire a feature parameter of the object within thecurrent frame for setting a first weighting, acquire a secondattribution predicted value of the object within the several previousframes, set a second weighting in accordance with the first weighting,and generate a first inducted attribution predicted value of the objectwithin the plurality of continuous frames via the first attributionpredicted value weighted by the first weighting and the secondattribution predicted value weighted by the second weighting.

The object attribution analyzing method and the object attributionanalyzing device of the present invention can utilize a large quantityof attribution predicted values within the frames to determine theaccurate attribution predicted result. In a conventional way, theattribution predicted value of the object within a monitoring region maybe varied violently or disappeared in some situations, such as theexposed image or the object being sheltered; thus, the conventional wayof utilizing a single frame to determine attribution of the objectcannot acquire a correct result. The object attribution analyzing methodof the present invention can utilize the same attribution predictedvalue of the same object within the previous frame and the current frameor the latest frame to effectively minimize the variability of theattribution predicted result. Even though attribution of the objecttraced within the current frame or the latest frame cannot be detectedand identified, a predicted value of the object's attribution can becontinuously output via the attribution induction result of the sameobject within the previous frames.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiment that isillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of variation of an attribution predicted value of anobject within continuous frames output by the camera in the prior art.

FIG. 2 is a functional block diagram of an object attribution analyzingdevice according to an embodiment of the present invention.

FIG. 3 is a flow chart of an object attribution analyzing methodaccording to the embodiment of the present invention.

FIG. 4 is a diagram of several frames arranged in a sequence of timeaccording to the embodiment of the present invention.

FIG. 5 is a diagram of an attribution predicted result generated by theobject attribution analyzing method according to the embodiment of thepresent invention.

FIG. 6 is a flow chart of the object attribution analyzing methodaccording to another embodiment of the present invention.

DETAILED DESCRIPTION

Please refer to FIG. 2. FIG. 2 is a functional block diagram of anobject attribution analyzing device 10 according to an embodiment of thepresent invention. The object attribution analyzing device 10 canutilize previous and latest analyzing information of a frame tracingresult to predict object attribution within the frame. The objectattribution analyzing device 10 can be a network video recorder (NVR)connected to a camera in a wire manner or in a wireless manner. Theobject attribution analyzing device 10 further can be modularizedequipment installed inside the camera. The object attribution analyzingdevice 10 can include a receiver 12 and a processor 14. As the objectattribution analyzing device 10 is the network video recorder, thereceiver 12 can receive a plurality of continuous frames from theexternal camera; as the object attribution analyzing device 10 is abuilt-in module of the camera, the receiver 12 can receive the pluralityof continuous frames acquired by an image capturing unit of the camera.The camera can provide an object tracing function, which can trace theobject inside the frame via face detection technology. The processor 14can be electrically connected to the receiver 12. The processor 14 canpredict the object attribution according to some or all tracing andanalyzing information of the continuous frames. The object attributionanalyzed and predicted by the present invention preferably can be afixed feature of the object.

Please refer to FIG. 2 to FIG. 5. FIG. 3 is a flow chart of an objectattribution analyzing method according to the embodiment of the presentinvention. FIG. 4 is a diagram of several frames arranged in a sequenceof time according to the embodiment of the present invention. FIG. 5 isa diagram of an attribution predicted result generated by the objectattribution analyzing method according to the embodiment of the presentinvention. The object attribution analyzing method illustrated in FIG. 3can be suitable for the object attribution analyzing device 10 shown inFIG. 1. The object attribution analyzing device 10 of the presentinvention can utilize the analyzing information of all the continuousframes Is′ to induct the attribution predicted result with greataccuracy. First, steps S300 and S302 are executed that the receiver 12can receive the plurality of continuous frames Is′, and the processor 14can divide the plurality of continuous frames Is′ into one current frameI(t) and several previous frames I(1)˜I(t−1). Then, steps S304 and S306are executed that the face detection technology is utilized to trace andcompute a first attribution predicted value of the object within thecurrent frame I(t), and acquire a feature parameter of the object withinthe current frame I(t) for setting a first weighting.

For example, if the object is a human being, a first attribution valuecan be human gender, and the feature parameter can be selected from agroup consisting of a blurred level of the object, a dimensional ratioof the object within the current frame I(t), an angle of the objectrelative to the camera, a distance of the object relative to the camera,and a combination thereof. If the face detection technology computes thefirst attribution predicted value of the human being, but detects apattern about the human is blurred due to interference of ambientillumination, or due to a pattern about the human face being small inthe frame, or due to a pattern about the human head showing a lateralface, the face detection technology can set the first weighting with alower value for the first attribution predicted value according to thefeature parameter within the current frame I(t). If the pattern aboutthe human is clear, the pattern about the human face has large ratio inthe frame, or the pattern about the human head shows a front face, theface detection technology can set the first weighting with a highervalue.

Then, steps S308 and S310 are executed that the processor 14 can acquirea second attribution predicted value of the object within the previousframes I(1)˜I(t−1), and set a second weighting in accordance with thefirst weighting. Generally, the second attribution predicted value canbe an induction result of a final frame I(t−1) and some earlier framesI(1)˜I(t−2) from the several previous frames I(1)˜I(t−1) respectivelyweighted by different weighting. A sum of the first weighting and thesecond weighting can be a specific value. The first weighting can beadjusted according to change of the feature parameter. The firstweighting can be inversely proportional to the second weighting; thesecond weighting can be computed by subtracting the first weighting fromthe specific value. For example, if the specific value equals 1.0, thefirst weighting can be 0.02 and the second weighting can be 0.98, or thefirst weighting can be 0.05 and the second weighting can be 0.95. Then,step S312 can be executed to generated a first inducted attributionpredicted value via the first attribution predicted value weighted bythe first weighting and the second attribution predicted value weightedby the second weighting. The first inducted attribution predicted valuecan be the attribution predicted result of the object at a point of timet. The first inducted attribution predicted value may be a sum value, anaverage value or a weighted mean value of the first attributionpredicted value weighted by the first weighting and the secondattribution predicted value weighted by the second weighting.Computation of the inducted attribution predicted value in the presentinvention is not limited to the above-mentioned embodiment, whichdepends on design demand.

Step S314 can be executed to acquire a latest frame I(t+1), and computea third attribution predicted value and a third weighting of the objectwithin the latest frame I(t+1). Then, steps S316 and S318 can beexecuted to set a fourth weighting in accordance with the thirdweighting, and generate a second inducted attribution predicted valuevia the third attribution predicted value weighted by the thirdweighting and the first inducted attribution predicted value weighted bythe fourth weighting. The second inducted attribution predicted valuecan be an attribution predicted result of the object at a point of time(t+1). When computing one attribution predicted value at any point oftime, the object attribution analyzing method can acquire theattribution predicted value of the object at one point of time and theattribution predicted value of the object during a previous period, andthe foresaid attribution predicted values can be respectively matchedwith different weighting for acquiring the induction result. Thus, thelatest attribution predicted result can be obviously influenced by theframe and the attribution predicted value acquired at the latest pointof time; in addition, frames acquired before the latest point of timeare represented as one unity, and the latest attribution predictedresult can be influenced by a total inducted attribution of the saidunity. The present invention does not analyze all the frames before thelatest point of time one-by-one, so as to economize an analysis periodand decrease a computation demand of the object attribution analyzingdevice 10.

As the frames shown in FIG. 4, when the object attribution analyzingmethod is actuated, the face detection technology is utilized to acquirean attribution predicted value A1 of the frame I(1) at the point of time(t=1). Then, the object attribution analyzing method can trace andcompute an attribution predicted value A2 of the frame I(2) at the pointof time (t=2), and set the weighting W2 according to the featureparameter of the object within the frame I(2); the weighting W1 can beset accordingly, and the attribution predicted value A1 weighted by theweighting W1 and the attribution predicted value A2 weighted by theweighting W2 can be used to generate an inducted attribution predictedvalue Ai at this point of time. At the point of time (t=3), the objectattribution analyzing method can trace and compute an attributionpredicted value A3 of the frame I(3), and set the weighting W3 accordingto the feature parameter of the object within the frame I(3); theweighting Wi can be set accordingly, and the attribution predicted valueA3 weighted by the weighting W3 and the inducted attribution predictedvalue Ai weighted by the weighting Wi can be used to generate aninducted attribution predicted value Ai′ at this point of time.Computation of the inducted attribution predicted value about otherpoints of time can be acquired via the above-mentioned process. All theframes are divided into one current frame (or one latest frame) andseveral previous frames, which are weighted by different weighting tocompute the latest attribution predicted value. As shown in FIG. 5,recording of the attribution predicted value output by the conventionalcamera which is illustrated as a curve C1 may be violently increased anddecreased among the frames; recording of attribution predicted valueoutput by the object attribution analyzing method of the presentinvention which is illustrated as a curve C2 can effectively decreaseits variability, and accuracy of the attribution predicted result can beincreased accordingly. In the embodiment, the attribution predictedvalue of the object within the frame acquired at the latest point oftime can provide large influence upon the latest attribution predictedresult, and its influenced efficacy is decided by the weighting aboutthe feature parameter of the object within the frame acquired at thelatest point of time.

Besides, the weighting can be adjusted according to time change. Asmentioned above, if an amount of the frame acquired before the latestpoint of time is less, the first weighting can be set as 0.05 and thesecond weighting can be set as 0.95, so the latest attribution predictedresult can be obviously influenced by the object attribution predictedvalue within the latest frame. If the amount of the frame acquiredbefore the latest point of time is more, the accuracy of the latestattribution predicted result may be preferred, so that the firstweighting can be set as 0.02 and the second weighting can be set as0.98, for preventing the latest attribution predicted result from beinginfluenced by the object attribution predicted value within the latestframe. Weighting adjustment of the present invention is not limited tothe above-mentioned embodiments; for instance, the lower weighting maybe applied to a situation with the less previous frames, and the higherweighting may be applied to the situation with the more previous frames,which depends on actual demand.

In other embodiment, the attribution predicted values of the objectwithin the frames at all points of time can be set as having the sameinfluence upon the latest attribution predicted result. For example, atone point of time (t=3), the object attribution analyzing method of thepresent invention may adjust the attribution predicted value A1, theattribution predicted value A2 and the attribution predicted value A3respectively by the weighting W1, the weighting W2 and the weighting W3,and a sum of those attribution predicted values weighted by thecorresponding weighting can be represented as the inducted attributionpredicted value at this point of time. That is to say, factors which mayinfluence the latest attribution predicted result can be averagelyproportioned to predicted object attribution within the frames acquiredat every point of time in this embodiment.

It should be mentioned that the object attribution analyzing method ofthe present invention can optionally filter the continuous frames Is′,and retain the frames with the feature parameter conforming to a demandfor induction process. Please refer to FIG. 2 and FIG. 6. FIG. 6 is aflow chart of the object attribution analyzing method according toanother embodiment of the present invention. The object attributionanalyzing method illustrated in FIG. 6 can be suitable for the objectattribution analyzing device 10 shown in FIG. 1. After step S300, theobject attribution analyzing method can optionally execute step S300-1to identify whether the feature parameter of the object within each ofthe continuous frames Is′ conforms to a threshold. If the featureparameter does not conform to the threshold, step S300-2 can be executedto filter one or some specific frames with the feature parameter notconforming to the threshold, and remove the specific frame from theprevious frames; therefore the previous frames are not continuous in asequence of time. If the frame with the feature parameter not conformingto the threshold is generated at the latest point of time, the weightingof the frame acquired at the latest point of time can be set as zero. Ifthe feature parameter conforms to the threshold, step S300-3 can beexecuted that the specific frame with the feature parameter conformingto the threshold can be set belonging to the previous frames or theframe acquired at the latest point of time, and then step S302 can beexecuted continuously.

For example, the threshold mentioned in step S300-1 can be a predefinedblurred value. If the blurred level of some frames is worse than thepredefined blurred value (which means the feature parameter does notconform to the threshold), it is difficult to acquire the accurateobject attribution predicted value within the said frames via the facedetection technology, so that the said frames can be removed forincreasing the accuracy of the latest attribution predicted result. Ifthe blurred level of the frames is better than the predefined blurredvalue, the face detection technology can acquire the accurate objectattribution predicted value within the said frames, so that the saidframes can be retained for being a benefit of tracing, predicting andinducting the attribution. In the embodiment, the threshold is notlimited to the blurred value, and further can be designed as thedimensional ratio of the object within the frame, and the angle or thedistance of the object relative to the camera, which depends onselection of the feature parameter.

Moreover, if an identifying result of the object attribution analyzingmethod determines that the feature parameter within the specific framedoes not conform to the threshold, step S300-2′ can be optionallyexecuted to weight the attribution predicted value of the object withinthe specific frame via a predefined weighting instead of filtering thespecific frame, and to assign that the specific frame belongs to theprevious frame or the frame acquired at the latest point of time. Instep S300-2′, it may be difficult to acquire the accurate objectattribution predicted value within the specific frame by the facedetection technology, but still may possibly acquire the correctattribution predicted value, so that the predefined weighting smallerthan other weighting (such as the first weighting and/or the secondweighting) can be utilized to decrease influence of the objectattribution predicted value within the specific frame upon the latestattribution predicted result. The object attribution analyzing methodmay execute steps S300-2 and S300-2′ alternatively, which depends onactual demand.

In conclusion, the object attribution analyzing method and the objectattribution analyzing device of the present invention can utilize alarge quantity of attribution predicted values within the frames todetermine the accurate attribution predicted result. In a conventionalway, the attribution predicted value of the object within a monitoringregion may be varied violently or disappeared in some situations, suchas the exposed image or the object being sheltered; thus, theconventional way of utilizing a single frame to determine attribution ofthe object cannot acquire a correct result. Comparing to the prior art,the object attribution analyzing method of the present invention canutilize the same attribution predicted value within the previous frameand the current frame or the latest frame to effectively minimize thevariability of the attribution predicted result. Even though attributionof the object traced within the current frame or the latest frame cannotbe detected and identified, a predicted value of the object'sattribution can be continuously output via the attribution inductionresult of the same object within the previous frames.

Those skilled in the art will readily observe that numerousmodifications and alterations of the device and method may be made whileretaining the teachings of the invention. Accordingly, the abovedisclosure should be construed as limited only by the metes and boundsof the appended claims.

What is claimed is:
 1. An object attribution analyzing method,comprising: dividing a plurality of continuous frames into a currentframe and several previous frames; utilizing face detection to track andcompute a first attribution predicted value of an object within thecurrent frame; utilizing the face detection to acquire a featureparameter of the object within the current frame for setting a firstweighting; acquiring a second attribution predicted value of the objectwithin the several previous frames; setting a second weighting inaccordance with the first weighting; and generating a first inductedattribution predicted value of the object within the plurality ofcontinuous frames via the first attribution predicted value weighted bythe first weighting and the second attribution predicted value weightedby the second weighting.
 2. The object attribution analyzing method ofclaim 1, further comprising: acquiring a latest frame after theplurality of continuous frames; utilizing the face detection to trackand compute a third attribution predicted value of the object within thelatest frame; utilizing the face detection to acquire the featureparameter of the object within the latest frame for setting a thirdweighting; setting a fourth weighting in accordance with the thirdweighting; and generating a second inducted attribution predicted valueof the object via the third attribution predicted value weighted by thethird weighting and the first inducted attribution predicted valueweighted by the fourth weighting.
 3. The object attribution analyzingmethod of claim 1, wherein the second attribution predicted value is aninduction result of a final frame and some earlier frames from theseveral previous frames respectively weighted by different weighting. 4.The object attribution analyzing method of claim 1, further comprising:searching a specific frame with the feature parameter not conforming toa threshold from the plurality of continuous frames; and removing thespecific frame from the previous frames.
 5. The object attributionanalyzing method of claim 4, wherein the first weighting equals zerowhen the feature parameter of the current frame does not conform to thethreshold.
 6. The object attribution analyzing method of claim 4,wherein the previous frames are not continuous during a time sequence.7. The object attribution analyzing method of claim 1, furthercomprising: searching a specific frame with the feature parameterconforming to a threshold from the plurality of continuous frames; anddefining the specific frame is one of the previous frames.
 8. The objectattribution analyzing method of claim 1, further comprising: searching aspecific frame with the feature parameter not conforming to a thresholdfrom the plurality of continuous frames; and generating the firstinducted attribution predicted value via an attribution predicted valueof the object within the specific frame weighted by a predefinedweighting.
 9. The object attribution analyzing method of claim 8,wherein the predefined weighting is smaller than the first weighting orthe second weighting.
 10. The object attribution analyzing method ofclaim 1, wherein the first attribution predicted value and the secondattribution predicted value are acquired by a fixed feature of theobject.
 11. The object attribution analyzing method of claim 1, whereinthe feature parameter is selected from a group consisting of a blurredlevel of the object, a dimensional ratio of the object within thecurrent frame, an angle of the object relative to a camera, a distanceof the object relative to the camera, and a combination thereof.
 12. Theobject attribution analyzing method of claim 1, wherein the firstweighting is adjusted according to change of the feature parameter, andthe first weighting is inversely proportional to the second weighting.13. The object attribution analyzing method of claim 1, wherein thefirst inducted attribution predicted value is a sum value, an averagevalue or a weighted mean value of the first attribution predicted valueweighted by the first weighting and the second attribution predictedvalue weighted by the second weighting.
 14. An object attributionanalyzing device, comprising: a receiver adapted to receive a pluralityof continuous frames; and a processor electrically connected to thereceiver, the processor being adapted to divide the plurality ofcontinuous frames into a current frame and several previous frames,utilize face detection to track and compute a first attributionpredicted value of an object within the current frame, utilize the facedetection to acquire a feature parameter of the object within thecurrent frame for setting a first weighting, acquire a secondattribution predicted value of the object within the several previousframes, set a second weighting in accordance with the first weighting,and generate a first inducted attribution predicted value of the objectwithin the plurality of continuous frames via the first attributionpredicted value weighted by the first weighting and the secondattribution predicted value weighted by the second weighting.
 15. Theobject attribution analyzing device of claim 14, wherein the processoris further adapted to acquire a latest frame after the plurality ofcontinuous frames, utilize the face detection to track and compute athird attribution predicted value of the object within the latest frame,utilize the face detection to acquire the feature parameter of theobject within the latest frame for setting a third weighting, set afourth weighting in accordance with the third weighting, and generate asecond inducted attribution predicted value of the object via the thirdattribution predicted value weighted by the third weighting and thefirst inducted attribution predicted value weighted by the fourthweighting.
 16. The object attribution analyzing device of claim 14,wherein the second attribution predicted value is an induction result ofa final frame and some earlier frames from the several previous framesrespectively weighted by different weighting.
 17. The object attributionanalyzing device of claim 14, wherein the processor is further adaptedto search a specific frame with the feature parameter not conforming toa threshold from the plurality of continuous frames, and remove thespecific frame from the previous frames.
 18. The object attributionanalyzing device of claim 14, wherein the processor is further adaptedto search a specific frame with the feature parameter conforming to athreshold from the plurality of continuous frames, and define thespecific frame is one of the previous frames.
 19. The object attributionanalyzing device of claim 14, wherein the processor is further adaptedto search a specific frame with the feature parameter not conforming toa threshold from the plurality of continuous frames, and generate thefirst inducted attribution predicted value via an attribution predictedvalue of the object within the specific frame weighted by a predefinedweighting.
 20. The object attribution analyzing device of claim 14,wherein the first inducted attribution predicted value is a sum value,an average value or a weighted mean value of the first attributionpredicted value weighted by the first weighting and the secondattribution predicted value weighted by the second weighting.